{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T12:09:48Z","timestamp":1793275788565,"version":"4.5.0"},"reference-count":9,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"Suppl 4","license":[{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,12]]},"abstract":"<jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>Dental caries, being an infectious, microbiologic disease, can cause dissolution and destruction of calcified tissues. Early detection of dental caries is therefore crucial for maintaining overall tooth health.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods:<\/jats:title>\n                    <jats:p>A randomized, single-blinded retrospective study was conducted. Three groups participated: dentists with &lt;10 years of experience, dentists with more than 10 years of experience, and the AI model. An external dataset of 20 anonymized bitewing radiographs served as the test set, reviewed and annotated by experienced dentists as the gold standard. Performance metrics included Intersection over Union, Confusion Matrix, Sensitivity, Precision, and F1 score.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>Among all, the AI model showed a high precision of 0.87, a recall of 0.77, and an F1 score of 0.82. This shows the AI model\u2019s superior precision in identifying caries and avoiding misinterpretations, like cervical burnout and Mach band effect.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion:<\/jats:title>\n                    <jats:p>The study achieved high diagnostic accuracy in identifying early carious lesions and offered advantages in efficiency and avoiding misinterpretations. Integrating AI with dental expertise could enhance diagnostic accuracy, optimize workflow, and support preventive interventions, ultimately improving patient care. Future research should focus on expanding the model\u2019s capabilities and exploring its seamless integration into clinical practice.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.4103\/jpbs.jpbs_1423_25","type":"journal-article","created":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T08:01:19Z","timestamp":1766044879000},"page":"S3364-S3366","update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":1,"title":["Development and Comparative Analysis of an AI Based Convolutional Neural Network Algorithm versus Dentists of Varying Experience in Detecting Dental Caries using Bitewing X-ray Images: An In vitro Study"],"prefix":"10.4103","volume":"17","author":[{"given":"Alwaleed Khalid","family":"Albadr","sequence":"first","affiliation":[{"name":"Postgraduate Resident, Department of Restorative Dentistry, Riyadh Elm University, Riyadh, Saudi Arabia"},{"name":"CEO, ThakaaMed, Riyadh, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"276","published-online":{"date-parts":[[2025,12,17]]},"reference":[{"key":"R2-20251218","first-page":"135","article-title":"Oral and Dental Manifestations in Noonan Syndrome","author":"Anthonappa","year":"2019","journal-title":"Science Direct"},{"key":"R3-20251218","doi-asserted-by":"crossref","first-page":"754","DOI":"10.1186\/s12903-024-04517-x","article-title":"Caries lesions diagnosis with deep convolutional neural network in intraoral QLF images by handheld device","volume":"24","author":"Tan","year":"2024","journal-title":"BMC Oral Health"},{"key":"R4-20251218","doi-asserted-by":"crossref","first-page":"7463","DOI":"10.1007\/s00784-023-05335-1","article-title":"Automatic caries detection in bitewing radiographs: Part I-deep learning","volume":"27","author":"Kunt","year":"2023","journal-title":"Clin Oral Investig"},{"key":"R5-20251218","doi-asserted-by":"crossref","first-page":"16807","DOI":"10.1038\/s41598-021-96368-7","article-title":"Deep learning for early dental caries detection in bitewing radiographs","volume":"11","author":"Lee","year":"2021","journal-title":"Sci Rep"},{"key":"R6-20251218","doi-asserted-by":"crossref","first-page":"104080","DOI":"10.1016\/j.jdent.2022.104080","article-title":"Cost-effectiveness of AI for caries detection: Randomized trial","volume":"119","author":"Schwendicke","year":"2022","journal-title":"J Dent"},{"key":"R7-20251218","doi-asserted-by":"crossref","first-page":"20220209","DOI":"10.1259\/dmfr.20220209","article-title":"Detection of the separated root canal instrument on panoramic radiograph: A comparison of LSTM and CNN deep learning methods","volume":"52","author":"Buyuk","year":"2023","journal-title":"Dentomaxillofac Radiol"},{"key":"R8-20251218","first-page":"2818","article-title":"Rethinking the inception architecture for computer vision","author":"Szegedy","year":"2016","journal-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition"},{"key":"R9-20251218","doi-asserted-by":"crossref","first-page":"103425","DOI":"10.1016\/j.jdent.2020.103425","article-title":"Detecting caries lesions of different radiographic extension on bitewings using deep learning","volume":"100","author":"Cantu","year":"2020","journal-title":"J Dent"},{"key":"R10-20251218","doi-asserted-by":"crossref","first-page":"126629","DOI":"10.1016\/j.neucom.2023.126629","article-title":"A review of deep learning in dentistry","volume":"554","author":"Huang","year":"2023","journal-title":"Neurocomputing"}],"container-title":["Journal of Pharmacy and Bioallied Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.ovid.com\/10.4103\/jpbs.jpbs_1423_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T15:46:43Z","timestamp":1787068003000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ovid.com\/10.4103\/jpbs.jpbs_1423_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12]]},"references-count":9,"journal-issue":{"issue":"Suppl 4","published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.4103\/jpbs.jpbs_1423_25","relation":{},"ISSN":["0976-4879","0975-7406"],"issn-type":[{"value":"0976-4879","type":"print"},{"value":"0975-7406","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12]]},"assertion":[{"value":"2025-08-08","name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-08-25","name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}}]}}